classifier/test_xgboost.py

import numpy as np
import xgboost as xgb
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

X_test = np.load("featureextraction/final_features/test_X.npy")
y_test = np.load("featureextraction/final_features/test_y.npy")

print("Test shape:", X_test.shape)

model = xgb.XGBClassifier()
model.load_model("classifier/xgboost_final_model.json")

y_pred = model.predict(X_test)

print("\nTEST SET RESULTS (FINAL MODEL)\n")
print("Accuracy:", accuracy_score(y_test, y_pred))

print("\nClassification Report:\n")
print(
    classification_report(
        y_test,
        y_pred,
        target_names=["Human", "AI"]
    )
)

print("\nConfusion Matrix:\n")
print(confusion_matrix(y_test, y_pred))